FUTURE OF THE FUTURE
Enterprise AI: working smarter and harder on behalf of professionals
As it turns out, enterprises across industries share similar needs where AI can augment the productivity and effectiveness of individual knowledge workers.
As more companies in more industries embrace AI, they will begin to use it to augment core business activities. Four key use cases shared across industries are customer support, expert assist, input management, and content discovery. • Customer support: This refers to the automation of customer interactions by voice or chatbots. In the enterprise, these virtual assistants are being developed to allow more complex dialogues with customers. Industry research firm Gartner predicts that, by 2020, the majority of commercial interactions will take place between customers and virtual agents. Though typically met with scepticism from customers, the use of chatbots and other virtual agents can be highly effective for automating low-level customer service inquiries. Autodesk, a global leader in computer-aided design and engineering software, has been immensely successful in this endeavour. Using the IBM Watson conversation service, the company built the autodesk virtual agent (AVA) that is able to answer 40 unique low-level queries at a rate of 30,000 interactions per month and cuts response time to customer inquiries from 1.5 days to five minutes, or just about a 99 per cent reduction.
• Expert assist: This refers to AI-based systems that enable knowledge workers to retrieve and produce information in a highly efficient manner. Typically, knowledge workers spend somewhere between 15 minutes and more than an hour per day searching for business information in outdated database systems and corporate intranets powered by keyword search technology. These return low-quality results in terms of the content or responsible domain expert they are seeking. AI can reduce this search time dramatically by using automatic clustering, ontologies, and visual-recognition technologies to identify for the knowledge worker the right information, content, and person. This reduces search time by 50 per cent or more.
“AI can help the logistics industry fundamentally shift its operating model”
• Input management: This refers to the automatic (pre-) processing of incoming mail, emails, invoices, spreadsheets, presentations, PDFs, and other documents with the help of AI. Most companies need to process large volumes of information on a daily basis. For example, the average knowledge worker sends and receives more than 120 email messages per day. Digitisation helps to reduce some of the burden: letters get scanned, invoices are entered into accounting software, presentations and spreadsheets are uploaded or accessed in shared drives in the cloud. But before this can happen, humans are typically involved – someone has to decide which department should receive the letter, which text block should be used, or who else needs to be involved. AI-based solutions can do some of this (pre-) processing. VKB, an insurance company in southern Germany, has implemented an AI-powered input management tool using IBM Watson to identify the topics and sentiment from unstructured text in incoming emails and letters. This information is used to prioritise and route these items to the correct departments.
• Content discovery: This refers to the automatic analysis of unstructured data from emails, PDFs, pictures, audio and video, made possible by the evolution of big data analytics tools and with the help of AI. Most companies have lots of this unstructured data – it typically represents 80 per cent of all company data and usually does not get analysed.
With the type of voice technology offered by AVRL, systems can interpret the semantic meaning and intent of a phrase and then connect the vocal mention of product names with product information contained within an ERP, WMS, or TMS system. This allows logistics operators to conversationally interact with their IT systems just as they would with another human being, even when using colloquial or informal phrasing.
The ability to automate input, store, and retrieve information via conversational voice interaction removes time and complexity from many warehouse tasks that require manual input or lookup of information.
In a world characterised by uncertainty and volatility, AI can help the logistics industry fundamentally shift its operating model from reactive actions and forecasting to proactive operations with predictive intelligence.
This section will identify both global, network-level prediction opportunities as well as process-specific prediction opportunities. Predictive network management using AI can significantly advance the performance of logistics operations.
For air freight, on-time and in- full shipment is critical as it represents only 1 per cent of global trade in terms of tonnage but 35 per cent in terms of value.
Most air freight lanes and networks are planned using historical data and expertise from professionals with decades of industry experience. DHL has developed a machine learning-based tool to predict air freight transit time delays in order to enable proactive mitigation.
By analysing 58 different parameters of internal data, the machine learning model is able to predict if the average daily transit time for a given lane is expected to rise or fall up to a week in advance.
“The need for predictive demand and capacity planning is self-evident in the fidget spinner boom of 2017”
Furthermore, this solution is able to identify the top factors influencing shipment delays, including temporal factors like departure day or operational factors such as airline on-time performance. This can help air freight forwarders plan ahead by removing subjective guesswork around when or with which airline their shipments should fly.
The need for predictive demand and capacity planning is self-evident in the fidget spinner boom of 2017. The three-paddle shaped spinning toy suddenly and unexpectedly sold an estimated 50 million units in a period of several months.
In the US, fidget spinners quickly shot up to 20 per cent of all retail toy sales in this period. This inundated air freight and express networks with shipment volumes as toy merchants rejected the normal lead times associated with ocean shipment of manufactured goods.
The first videos of teenagers doing tricks with fidget spinners began trending on YouTube in February 2017.
Hidden deep within online browsing data, YouTube video views, and conversations on social media, AI in its current state is able to identify both the quantitative rise in interest in a topic, as well as the context of that interest from semantic understanding of unstructured text. This enables predictions to be made about which fads could boom in a similar fashion to fidget spinners.
Thanks to the speed and efficiency of global supply chains and express networks, even a few weeks’ lead time provides significant advantage to merchants facing unexpected spikes in demand.
DHL’s Global Trade Barometer is a unique early indication tool for the current state and future development of global trade. The tool uses large amounts of operational logistics data, advanced statistical modelling, and artificial intelligence to give a monthly outlook on prospects for the global economy.
The model takes a bottom-up approach and uses import and export data of intermediate and early-cycle commodities from seven countries to serve as the basis input for the system, measured in air freight and containerised ocean freight levels.
Overall, the system regularly evaluates 240 million variables from seven countries (China, Germany, Great Britain, India, Japan, South Korea, and the US) that represent 75 per cent of global trade.
An AI engine, together with other non-cognitive analytical models, expresses a single value to represent the weighted average of current trade growth and the upcoming two months of global trade.
Tests with historical data reveal a high correlation between the DHL Global Trade Barometer and real containerised trade, providing an effective three month outlook for global trade.
Predictive risk management is critical for ensuring supply chain continuity. The DHL
Resilience360 platform is a cloud-based supply chain risk-management solution that has been tailored to the needs of global logistics operators.
For supply chain leaders in many industries, including the automotive, technology, and engineering and manufacturing sectors, managing the flow of components from thousands of worldwide suppliers is a regular part of daily business.
Problems with suppliers, from material shortages to poor labour practices and even legal investigations, can cause critical disruptions in the supply chain. The Resilience360 Supply Watch module demonstrates the power of AI to mitigate supplier risks.
Using advanced machine learning and natural language processing techniques, Supply Watch monitors the content and context of 8 million posts from over 300,000 online and social media sources. This allows the system to understand the sentiment of online conversations from unstructured text to identify indicators of risk ahead of time. This, in turn, allows supply chain managers to take corrective action earlier, and avoid disruption.
Intelligent route optimisation is critical for logistics operators to efficiently transport, pick up, and deliver shipments. Logistics providers and last-mile delivery experts typically have deep explicit and implicit knowledge of cities and their physical characteristics. However, new customer demands such as time-slot deliveries, ad-hoc pickups and instant delivery are creating new challenges with intelligent route optimisation.
Deutsche Post DHL Group pioneered the SmartTruck routing initiative in the early 2000s to develop proprietary real-time routing algorithms for its fleet operators and drivers.
Recently, new soft infrastructure of cities, such as digital and satellite maps, traffic patterns, and social media check-in locations are creating a wealth of information that can augment systems like SmartTruck and improve the overall routing of truck drivers on delivery runs.
Satellite imagery company DigitalGlobe delivers high-resolution pictures of the planet’s surface to ride-sharing giant Uber. These images provide rich input sources for the development of advanced mapping tools to increase the precision of pick up, navigation, and drop off between its drivers and riders.
DigitalGlobe’s satellites can decipher new road-surface markings, lane information, and street-scale changes to traffic patterns before a city adds them to its official vector map.
This level of detail from satellite imagery can provide valuable new insight to planning and navigating routes not only for the transport of people but for shipments as well.
AI-POWERED CUSTOMER EXPERIENCE
The dynamic between logistics providers and customers is changing.
For most consumers, touch points with a logistics company begin at checkout with an online retailer and end with a successful delivery or sometimes a product return. For businesses, touch points with logistics providers are characterised by long-term service contracts, servicelevel agreements, and the operation of complex global supply chains. AI can help personalise all of these customer touch points for logistics providers, increasing customer loyalty and retention. Voice agents can significantly improve and personalise the customer experience with logistics providers.
In 2017, DHL Parcel was one of the first last-mile delivery companies to offer a voice-based service to track parcels and provide shipment information using Amason’s Alexa. Customers with an Amazon Echo speaker in their home can simply ask things like “Alexa, where is my parcel?” or “Ask DHL, where is my parcel.” Customers can then speak their alphanumeric tracking number and receive shipment updates. If there is an issue with a shipment, customers can ask DHL for help and be routed to customer assistance.
Taking this one step further, Israeli startup package.ai has developed Jenny, a conversational agent to assist with
last-mile delivery. Jenny can contact parcel recipients via Facebook Messenger or SMS to coordinate delivery times, locations, and other specialised instructions.
The chat-based service can also send driver progress updates and last-minute changes, as well as close the loop with delivery confirmation and gathering feedback from customers.
The conversational capabilities and context Jenny is able to process makes for a natural touch point for customers, as well as cutting down on up to 70 per cent of operational costs through route optimisation and successful first-time delivery.
AI can enable logistics companies to be proactive about managing their customer relationships. Already today, hedge fund organisations like Aidyia and Sentient Technologies are using AI to explore market data and make stock trades autonomously; each day, after analysing everything from market prices and volumes to macroeconomic data and corporate accounting documents, their respective AI engines make market predictions and then “vote” on the best course of action.
Initial trials showed a 2 per cent return on an undisclosed sum which, while not statistically significant, represents a significant shift in how firms can conduct research and execute trades.
Anticipatory Logistics takes the AI-powered logistics customer experience to the next level, delivering goods to customers before they have even ordered them or realised they needed them. Anticipatory logistics seeks to leverage the capabilities of AI to analyse and draw predictions from vast amounts of data such as browsing behaviour, purchase history, and demographic norms as well as seemingly unrelated data sources such as weather data, social media chatter, and news reports to predict what customers will purchase.
Exposing these data sources to AI analysis, companies can effectively predict demand and shorten delivery times by moving inventory closer to customer locations and allocating resources and capacity to allow for previously unforeseen demand. In some cases, it would even require having non-purchased inventory constantly in transit to allow for instant delivery for an order placed while the goods are in motion.
Artificial intelligence is once again set to thrive; unlike past waves of hype and disillusionment, today’s current technology, business, and societal conditions have never been more favourable to widespread use and adoption of AI.
In the consumer world, AI is already here to stay. Among businesses, leading industries such as tech, finance, and, to a lesser extent mobility, are well into their AI journey.
Industrial enterprise sectors like logistics are beginning theirs in earnest now. Drawing on learning from the consumer, enterprise, retail, mobility, and manufacturing sectors provides valuable foresight of how AI
“Enterprise AI will alleviate burdensome tasks that define many aspects of modern working life”
can be productively applied in logistics. Enterprise AI will alleviate burdensome tasks that define many aspects of modern working life. As big data from operational, public, and private sources becomes exposed to and processed by AI, the logistics networks will shift to a proactive and predictive paradigm. Computer vision and language-focused AI will help logistics operators see, understand, and interact with the world in novel, more efficient ways than before.
These same AI technologies will give rise to a new class of intelligent logistics assets that augment human capabilities. In addition, AI can help logistics providers enrich customer experiences through conversational engagement, and deliver items before customers have even ordered them.
AI, however, it is not without its challenges. The bias and intent of each AI developer can become intertwined in the system’s decision-making functions, raising complex questions about the ethics of AI models. Here, business, society, and government bodies will need to develop standards and regulations to ensure the continued progress of AI for the benefit of humanity.
We believe the future of AI in logistics is filled with potential.
As supply chain leaders continue their digital transformation journey, AI will become a bigger and inherent part of day-to-day business, accelerating the path towards a proactive, predictive, automated, and personalised future for logistics.
Ultimately, AI will place a premium on human intuition, interaction, and connection allowing people to contribute to more meaningful work.
In a 2017 survey of CEOs, there was an almost even four-way split among leaders who said they were using AI, thinking of using AI, have heard of AI, or believe AI is not a priority.
This begs the question: who will teach our business leaders about AI?
DHL and IBM believe the time for AI in logistics is now. We look forward to hearing from you and creating opportunities for collaboration and joint exploration using AI in your organisation.
Below: Taxonomy of machine learning methodologies
Above: AI in the internet of things